Cross Domain Semantic Segmentation

Cross-domain semantic segmentation aims to adapt a segmentation model trained on one dataset (the source domain) to accurately segment images from a different, unlabeled dataset (the target domain). Current research heavily focuses on mitigating domain discrepancies through techniques like adversarial training, self-training with pseudo-labels, and leveraging the semantic knowledge embedded in large vision-language models or diffusion models. These advancements are crucial for improving the robustness and generalizability of semantic segmentation models, enabling their application in scenarios with limited labeled data across diverse visual domains, such as medical imaging, remote sensing, and autonomous driving.